Identification of key genes associated with alcohol addiction and DNA replication aberrant in Ovarian serous cystadenocarcinoma using an integrated bioinformatics analysis

doi:10.21203/rs.3.rs-3227033/v1

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Identification of key genes associated with alcohol addiction and DNA replication aberrant in Ovarian serous cystadenocarcinoma using an integrated bioinformatics analysis

https://doi.org/10.21203/rs.3.rs-3227033/v1

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Background

Multiple evidence indicates a significant correlation between alcohol and DNA replication aberrant in cancer, but the role of this correlation in Ovarian serous cystadenocarcinoma (OSC) remains insufficient. This research evaluated correlation between DNA replication related genes (DRRGs) and alcohol addiction related genes (AARGs) in OSC via bioinformatics

Methods

Multiple bioinformatics approaches were used to confirm the diagnosis, prognosis, and treatment significance of DRRGs in OSC. The effect of MCM3 on OSC proliferation and DNA replication were confirmed by MTT and EdU analysis.

Results

the level of ORC2/4, LIG1, RNASEH2B/C, RFC1, POLE4 and POLD4 was significantly decreased in OSC, but other DRRGs was obviously increased in OSC samples compared to normal samples. PCA analysis indicated that these DRRGs could be biomarkers for early diagnosis in OSC. PRIM2, ORC3, POLD1, POLD2, MCM3, RPA2, GMNN and RAD52 were identified as prognostic signatures. High-risk group has a poor prognosis. MCM3 was a key gene in the DRRG and AARGs in the development of OSC, which was enhanced in OSC patients EVs, and promoted the DNA replication and proliferation.

Conclusion

The hub gene MCM3 represent a significant gene involved in alcohol addiction and DNA replication aberrant for OSC progression.

Alcohol addiction

Ovarian serous cystadenocarcinoma

Bioinformatic analysis

DNA replication

immune infiltration

Ovarian cancer is a serious malignant ovary disease, and is largely asymptomatic in its early stage in women worldwide, resulting in a huge social and health burden(1). Ovarian serous cystadenocarcinoma (OSC) is a primary type of ovarian cancer with approximately 75–80% of the incidence(2). Additionally, a large proportion of OSC patients are usually diagnosed at advanced stages, leading to the neoplasm invades the peritoneal surface(3). However, for those who present with distant metastasis or relapse, the survival rate was as low as 30%, but the five-year survival rate could be higher than 90%(4). Hence, validation and searching of possible diagnostic and prognostic biomarkers for OSC patients have tremendous developing potentiality.

A large number of literature reports indicated that excessive alcohol intake is associated with multiple cancer risk(5, 6). In OSC patients, alcohol is also a key risk in OSC patients among alcoholic women(7). Excessive alcohol intake promotes a variety of tumor malignant phenotypes, such as proliferation, apoptosis escaping, growth, metastasis, angiogenesis, immune infiltration, metabolic reprogramming(8). However, the exact molecular mechanism about excessive alcohol intake in OSC is still unknown.

DNA replication is an all or none progression in cell division(9). Once the DNA replication begins it proceeds to completion(10). Eukaryotic cells utilize DNA replication licensing complex to maximally control DNA replication, including ORC complex, MCM complex, CDC6, CDT1, and other significant proteins(11). High levels of DNA replication might assist in carcinogenesis by the enlargement of gene alteration effects and promotion of genetic variation probability(12). Ectopic expression of DNA replication related genes (DRRGs) is an early phenomenon during cancer occurrence and progression in multiple cancer types, including colon cancer, breast cancer, lung cancer, esophagus cancer and so on(13–16). In our previous study, we also found high level of DRRGs in liver cancer(17) and ovarian cancer patients(18), which could be significantly prognostic and diagnostic biomarkers. However, the molecular mechanisms of aberrant DNA replication have not been clearly deciphered, especially in OSC. High level of DRRGs could activate multiple DNA replication origins licensing, resulting in genomic instability via replication stress(19). Previous studies indicated that DRRGs knockdown could increase the chemosensitivity for cisplatin, 5-Fluorouracil, hydroxyurea etc(20–22). Therefore, these results indicated that DRRGs might be a “Saint” and “Sinner” for patients with cancer.

The aims of this study were to confirm the potential scientific value and clinical application significance of DRRGs in diagnostic, prognostic, and therapeutic stratification for OSC patients. We systematically confirmed the expression level, survival significance, DNA alteration, functional enrichment, protein structures, risk score, cluster classification, immune infiltration, ferroptosis level, m6A modification, stemness features and drug sensitivity for DRRGs in OSC patients. The present study was firstly identified the significance of DRRGs in OSC by multiple bioinformatic algorithms (Fig. 1).

Data download

We extracted level three FPKM data of 374 cases of OSC patients from TCGA database (https://portal.gdc.cancer.gov/)(23), including concerning gene expression, copy number variation, SNPS and other data. The mRNA expression data of 88 normal ovary data were download from GTEx database (https://www.gtexportal.org/)(24). The alcohol addiction related genes were confirmed from GEO database GSE20489 dataset(25). 43 DRRGs sets were extracted from KEGG database (https://www.kegg.jp/)(26).

DNA alteration analysis

The cBioProtal database (http://www.cbioportal.org/)(27) was used to confirm the DNA alteration frequency of DRRGs, and the association between these genes alteration and survival outcome in OSC patients.

Functional enrichment analysis

We used the DAVID database (https://david.ncifcrf.gov/)(28) to make the GO and KEGG enrichment analysis. For the specific research methods, please refer to our previous study(29).

Tertiary structure of protein and PPI network construction

The Protein Data Bank (PDB) (https://www.rcsb.org/)(30) was used to construct the tertiary structures of DRRGs. We utilized GeneMANIA 3.6.0 (http://www.genemania.org)(31) to establish the PPI network based on these DRRGs. For the specific research methods, please refer to our previous study(18).

Survival analysis

The survival analysis for these DDRGs was used for the KMplot database (https://kmplot.com/analysis/)(32).

Prognosis Model Construction

Firstly, the expression profiles and clinical information for these OSC patients were extracted from TCGA database. Converting counts data to TPM and normalizing the data log2 (TPM + 1), keeping samples with clinical information at the same time. Finally, there are 374 samples for subsequent analysis. Log-rank test was used to compare differences in survival between these groups. The timeROC(v 0.4) analysis was used to compare the predictive accuracy of risk score. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for feature selection, 10-fold cross-validation was used, and the R package glmnet was used for the analysis.

Classification of OSC subtypes

ConsensusClusterPlus R package (v1.54.0) was used to make Consistency analysis, the maximum number of clusters is 2, and 80% of the total sample is drawn 100 times, clusterAlg = "hc", innerLinkage='ward.D2'. Use the R software package pheatmap (v1.0.12) for clustering heatmaps. The gene expression heatmap retains genes with SD > 0.1. If the number of input genes is more than 1000, it will extract the top 25% genes after sorting the SD.

Immunoinfiltration analysis

The R software ggstatsplot package was used to draw the correlations between gene expression and immune score, the R software pheatmap package was used to draw multi-gene correlation.

Stemness index analysis

Use the OCLR algorithm to calculate mRNAsi which constructed by Malta et al(33). Based on the mRNA expression signature, the gene expression profile contains 11,774 genes. We used the same Spearman correlation.

IC50 score analysis

Predicted the chemotherapeutic response for OSC based on the largest publicly available pharmacogenomics database (https://www.cancerrxgene.org/)(34). The prediction process was implemented by R package “pRRophetic”. The samples' half-maximal inhibitory concentration (IC50) was estimated by ridge regression. Using the batch effect of combat and tissue type of all tissues, and the duplicate gene expression was summarized as mean value.

Cell culture and transfection

A2780 was purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). These cells were maintained in RPMI 1640 medium (Sigma Aldrich; Thermo Fisher Scientific, Inc.) with 10% fetal bovine serum (FBS) (FBS; Gibco; Invitrogen; Thermo Fisher Scientific, Inc.), 100 IU/mL penicillin, and 10 µg/mL streptomycin (Thermo Fisher Scientific, Inc.). All cells were cultured at 37°C with 5% CO2.

Proliferation analysis

For Edu and MTT analysis, the detailed methods are described in our previous article(35).

Western blot

The detailed methods are described in our previous article(29). The primary antibodies used were as follows: MCM3 (Proteintech, Cat No: 15597-1-AP, at 1/2000 dilution), and GAPDH (Proteintech, Cat No. 10494-1-AP, at 1/25000 dilution).

Statistical AnalysisAll statistical analyses were conducted in the R language (version 3.6). All the statistical tests were bilateral, and p < 0.05 was statistically significant.

Confirming the expression of DRRGs in OSC

At first, we download the expression profiles of 379 OSC patient tissue samples based on TCGA database, and the datasets of 88 normal ovary tissue samples based from GTEx database (Fig. 2A-B). PCA analysis indicated that OSC (blue) and normal (red) tissues could be well discriminated by the expression profiles of these DRRGs (Fig. 2C). Moreover, we found the expression of MCM5, POLD2 and RPA1 was not significantly changed in OSC samples compared to normal ovary samples, the level of ORC2/4, LIG1, RNASEH2B/C, RFC1, POLE4 and POLD4 was significantly decreased in OSC, but other DRRGs was obviously increased in OSC samples compared to normal samples (Fig. 3). These results suggested the aberrant DNA replication was played a key role in the development and progression of OSC.

DNA alteration of DRRGs in OSC

Subsequently, we detected the DNA alteration level of these DRRGs in OSC based on cBioProtal database. The OSC dataset suggested DNA alteration percentages of these DRRGs were 4% (POLA1), 2.9% (POLA2), 1.3% (PRIM1), 1.6% (PRIM2), 2.3% (POLD1), 2.6% (POLD2), 12% (POLD3), 5% (POLD4), 2.9% (POLE), 1.9% (POLE2), 1.3% (POLE3), 2.6% (POLE4), 6% (ORC1), 4% (ORC2), 2.6% (ORC3), 1.9% (ORC4), 4% (ORC5), 1.6% (ORC6), 6% (MCM2), 3% (MCM3), 6% (MCM4), 1.9% (MCM5), 1.3% (MCM6), 3% (MCM7), 2.6% (RFC1), 4% (RFC2), 2.3% (RFC3), 23% (RFC4), 1.6% (RFC5), 2.3% (RPA1), 1.6% (RPA2), 3% (RPA3), 1.6% (RPA4), 3% (DNA2), 1.6% (FEN1), 1.6% (LIG1), 5% (RNASEH1), 12% (RNASEH2A), 3% (RNASEH2B), 2.9% (RNASEH2C), 1.9% (CDC6), 8% (PCNA), and 5% (CDT1) (Fig. 4A). These DRRGs alteration were primely enriched in amplification in OSC patients (Fig. 4B), which partly explained the upregulation of DNA replication in OSC. However, the survival analysis indicated that the prognosis was not correlated with the DNA alteration of DRRGs (Fig. 4C).

Function of these DRRGs in OSC

We constructed the three-dimensional structure of key DRRGs in DNA replication via the PDB database. The result showed that ORC complex, MCM complex, CDC6 and CDT1 could directly interacted with each other, which binds to DNA to activate replication. Moreover, RFC1-5 directly interact with PCNA to accelerate the extension of DNA (Fig. 5A). We also made a correlation analysis among these DRRGs in normal ovary, OSC, and normal ovary plus OSC datasets based on TCGA and GTEx database. The stratification and clustering of these DRRGs was markedly observed in OSC dataset and normal ovary plus OSC datasets, but the level of DRRGs were not significantly correlated with each other in normal ovary (Fig. 5B), indicating the level of DNA replication might be obviously enhanced in OSC by the mutual feedback regulation in these DRRGs. We further constructed the PPI network for these DRRGs based on GeneMANIA database, including RNASEH2C, POLE4, RNASEH1, RNASEH2B, RPA4, DNA2, RFC1, POLD3, RNASEH2A, ORC6, PRIM2, POLE3, POLA2, ORC4, RFC5, POLE, POLE2, POLD4, ORC5, LIG1, POLA1, PRIM1, RFC2, CDT1, ORC2, RFC3, ORC3, POLD1, POLD2, FEN1, RFC4, MCM4, ORC1, MCM5, MCM6, MCM7, MCM2, CDC6, MCM3, PCNA, RPA2, RPA3, RPA1, CDC45, DBF4, CDC7, MCM10, CLSPN, MCM8, ATRIP, GINS2, GINS4, GINS1, RAD9B, RAD1, RAD17, HUS1, GMNN, POLB, CDK2, RAD9A, PPIA and RAD52 (Fig. 5C).

Further GO enrichment analysis indicated that these genes were significantly enriched in cellular process, metabolic process, response to stimulus, cell proliferation, reproductive process and immune system process in Biological process term, remarkedly enriched in cell part, protein-containing complex, cell junction, nucleoid, and supramolecular complex in Cellular component term, signally enriched in binding, catalytic activity, molecular function regulator, and transcription regulator activity in Molecular function term (Fig. 5D).

The effect of DRRGs on signaling pathways

KEGG enrichment analysis indicated that these genes were enriched in DNA replication, nucleotide excision repair, mismatch repair, cell cycle and base excision repair (Fig. 6A). All the DRRGs promote DNA replication was shown in Fig. 6B, which is the classical molecular function for these DRRGs. RFC1-5, PCNA, RPA1-4, POLD1-4, and LIG1 were involved in mismatch repair progression (Fig. 6C). Both RPA1-4, POLD1-4, POLE1-4, RFC1-5, PCNA and LIG1 played key roles in nucleotide excision repair progression (Fig. 6D). POLE1-4, POLB, POLD1-4, PCNA, FEN1 and LIG1 were enriched in base excision repair (Fig. 6E). ORC complex, MCM complex, CDK2, PCNA, CDC6, CDC45, CDC7, and DBF4 were involved in the progression of cell cycle (Fig. 6F). These results indicated that DRRGs have an important function in genome stability.

Analysis of DRRGs expression identifies two distinct subgroups of OSC

Next, we used consensus clustering analysis to classify OSC tissue samples into different DRRG patterns, and two distinct DRRG clusters were identified using unsupervised clustering (Fig. 7A). Two cluster group have significantly differently expressed genes (Fig. 7B). Hence, we extracted these differently expressed genes by Volcano maps (Fig. 7C) and heat maps (Fig. 7D). KEGG enrichment analysis indicated that these differently expressed genes were remarkedly involved in p53 signaling pathway, pyrimidine metabolism, purine metabolism, progesterone mediated oocyte maturation, platinum drug resistance, and mismatch repair (Fig. 7E). GO enrichment analysis found that these differently expressed genes were significantly enriched in spindle organization, sister chromatid segregation, regulation of mitotic cell cycle phase transition, regulation of cell cycle phase transition, and DNA replication (Fig. 7F).

Furthermore, we found m6A methylation related genes were significantly increased in cluster 1 group compared to cluster 2 group, including, METTL3, METTL14, WTAP, VIRMA, RBM15, RBM15B, YTHDC1, YTHDC2, YTHDF3, YTHDF1, YTHDF2, HNRNPC, IGF2BP1, IGF2BP2, IGFBP3, RBMX, HNRNPA2B1, FTO and ALKBH5 (Fig. 8A). The ferroptosis related genes was significantly and differently expressed in cluster 1 compared to cluster 2, especially in HSPA5, NFE2L2, HSPB1, FANCD2, FDFT1, SLC1A5, TFRC, RPL8, NCOA4, LPCAT3, GLS2, CS, CARS, ALOX15, ACSL4 and AIFM2 (Fig. 8B). Immuno-infiltration analysis suggested that CD4 T cell was highly infiltrated in cluster 1, but CD8 T cell, and Myeloid dendritic cell were lowly infiltrated in cluster 1 (Fig. 8C). The immune checkpoint of SIGLEC15 was highly expressed in cluster 1 (Fig. 8D). The stemness feature analysis showed that cluster 1 had high level of stemness compared to cluster 2 (Fig. 8E). Drug sensitively analysis indicated that the cluster 1 was more sensitively for cisplatin and docetaxel (Fig. 8F). These results indicated that the OSC patients of cluster 1 group might has a more malignant phenotype but more sensitive to cisplatin and docetaxel than cluster 2 group.

The prognostic values for these DRRGs in OSC

Moreover, we download the prognostic profiles from TCGA database in Fig. 5. The survival analysis showed that ORC3/6, MCM3/6, PRIM1/2, POLD2/3, RFC4, RNASEH2A and FEN1 were positively correlated with prognosis in OSC patients (Fig. 9).

Identification of clinical outcomes in OSC based on these DRRGs

For further confirming the clinical outcomes based on these DRRGs in OSC patients, we utilized these 43 DRRGs to construct the prognostic model (lambda.min = 0.0493). The Riskscore=(-0.0277)*PRIM2+(-0.0949)*ORC3+(0.0395)*POLD1+(-0.1048)*POLD2+(-0.0244)*MCM3+(0.0766)*RPA2+(-0.11)*GMNN+(0.0558)*RAD52 (Fig. 10A-B). Then, these OSC patients were divided into high or low risk group via the median risk score (Fig. 10C). The survival analysis showed that the survival time and rate were significantly decreased in high risk group OSC patients compared to low risk OSC patients (Fig. 10D). The ROC curve value was 0.644, 0.631 and 0.6624, respectively, in AUCs at one, two, and three years (Fig. 10E). These results indicated that this prognostic model based on DRRGs had a significant prognostic values in survival monitoring. Furthermore, we confirmed the correlation between risk score and the expression levels of multiple immune cell types in OSC. The result showed that the level of CD8 T, NK, and other uncharacterized cell was decreased, as the risk score increased, but the expression of macrophage was increased, as the risk score increased (Fig. 10F).

Validation of hub DRRGs and AARGs in OSC

Then, we confirmed the DEGs in GSE20489 (Fig. 11A&B). The KEGG analysis indicated that these DEGs were enriched in herpes simplex virus 1 infection, amyotrophic lateral sclerosis, Alzheimer disease, huntington disease, autophagy and T cell receptor signaling pathway (Fig. 11C). GO enrichment showed that these DEGs were enriched in neutrophil activation involved in immune response, neutrophil degranulation, RNA splicing, RNA catabolic process, and proteasome-mediated ubiquitin-dependent protein catabolic process (Fig. 11D). We used Venn analysis found four key gene based on hub DRRGs with prognosis values and hub AARGs, which indicated that the levels of MCM3 and RPA2 were both obviously increased in OSC blood samples based on exoRBase database (Fig. 11E). Moreover, the expression of MCM3 was significantly enhanced in the OSC blood samples compared to heathy blood samples (Fig. 11F), which indicated MCM3 might be a key protein to promote OSC progression by alcohol addiction.

MCM3 might facilitate OC cell DNA replication and proliferation by EVs

We further utilized CCLE database to confirm the level of MCM3 in multiple OSC cancer cell lines (Fig. 11G). We constructed MCM3 knockdown A2780 cell lines and detected by western blot (Fig. 11H). MTT analysis indicated MCM3 inhibiting with MCM3 shRNA#1 and #2 could significantly inhibit the proliferation ability for A2780 cell (Fig. 11I). EdU analysis showed that MCM3 knockdown could significantly reduce the DNA replication level (Fig. 11J). Taken together, these results indicated that alcohol might be a key factor in promoting OSC cell proliferation and DNA replication by upregulating MCM3 expression.

In ovarian cancer, the most common pathological type is OSC(36). The diagnosis, treatment and prognosis of this disease require further clinical improvement(1). The exploration of aberrant DNA replication opens up a significant direction for OSC patients(11). The aberrant DNA replication is one of the most common cancer hallmarks and resulting in the cause of uncontrolled cell proliferation(12). Most differentiated cells in the body are in growth-arrested state under normal physiological conditions, indicating that these cells are in G0 phase and the proteins of DRRGs are in inactivated state(37). Moreover, many studies suggested that the ectopic expression of DNA replication proteins was observed in multiple cancer types, such as pancreatic cancer(38), prostate cancer(39), cervical cancer(40), endometrial cancer(41), liver cancer(17) and breast cancer(42). Based on our previous studies, we found MCM complex might be a potential target and prognostic marker for OSC patients(18). However, there is still no evidence for confirming both DRRGs in OSC progression. In this study, we found the 43 DRRGs expression in OSC, which indicated that only 3 DRRGs (MCM5, POLD2 and RPA1) were not significantly changed in OSC samples, 8 DRRGs (ORC2/4, LIG1, RNASEH2B/C, RFC1, POLE4 and POLD4) were down-regulated in OSC samples, and other 32 DRRGs were both significantly up-regulated in OSC samples. Further PCA analysis indicated that these DRRGs could be excellent diagnostic markers for OSC patients, which was also consistent with previous study(18, 43, 44).

For the prognostic significance of DRRGs, we found the mRNA levels of ORC3/6, MCM3/6, PRIM1/2, POLD2/3, RFC4, RNASEH2A and FEN1 were positively correlated with prognosis in OSC patients. However, there is no research reported correlation between the protein expression of these DRRGs and OSC patient’s prognosis. Li et al found that MCM3 protein expression was negatively correlated with the prognosis in liver cancer patients(45). Our previous study indicated that both MCM3/6 mRNA level were negatively correlated with the prognosis of liver cancer patients(17). Wang et al found that ORC6 was correlated with the poor prognosis for liver cancer patients(46). Ma and his colleagues confirmed that the PRIM2 and MCM6 was positively associated with cervical patients’ prognosis, but MCM3 was negatively correlated with these patient’s prognosis(47). Wang et al found that high level of RFC4 protein expression was correlated with poor prognosis for colorectal cancer(48). Shen et al indicated that RNASEH2A was negatively correlated with kidney cancer patients(49). Zhao et al. found that FEN1 was associated with favorable prognosis in gastric cancer(50). These results indicated that DRRGs might play dual roles in different cancer types. For further explore the prognostic significance of DRRGs in OSC, we constructed the prognostic model. The DRRGs prognostic model could forecast OSC patient outcomes more accurately than individual genes. Moreover, DRRGs might regulate the immune infiltration to added the mortality risk in OSC patients. Many studies found the potential association between aberrant DNA replication and immune infiltration in ovarian cancer(51–53).

We also found that the significantly correlation among these DRRGs in OSC samples and OSC plus normal ovary samples, but the correlation was not observed in the normal ovary samples. These results indicated that there may be one or more mechanisms for some kind of positive feedback regulation among these DRRGs during ovary tumorigenesis. When this happens, there is a strong cascade effect that amplifies the effects of abnormal DNA replication via inducing ectopic expression of DRRGs. Muhammad et al. indicated that multiple oncogene driven the overexpression of DRRGs, especially in TP53, MYCN, E2F, CCND1, ATR, CCNE and ATM(11). This perspective could also explain why DRRGs are so strongly correlated in OSC tissues compared to normal ovary tissues.

In GO and KEGG enrichment analysis, we found these DRRGs were involved in DNA replication, multiple DNA repair ways, and cell cycle. This is also consistent with previous reports indicating that CtBP1/2 could drive DRRGs expression to regulate DNA replication fork, DNA damage response, cell cycle and apoptosis in ovarian cancer(54). Praveen et al found that Kub5-Hera/RPRD1B interactome could drive expressions of MCM proteins, PCNA and POLD to regulate DNA mismatch repair, resulting in maintaining genome stability(55). Kim and his colleagues suggested that metformin could suppress the protein expression of DRRGs to enhance chemosensitivity for 5-FU(56). These results supported our results, and indicated DRRGs might be excellent therapeutic targets for OSC patients.

Cisplatin is utilized as first-line treatment for OSC(57). However, side effects including chemoresistance and nephrotoxicity greatly limit its use(58). Precision medicine is becoming a significant part of OSC therapy, which could avoid drug side effects and resistance to the most extent(59). To consider precision therapy and further explore the potential of DRRGs as therapeutic targets in OSC, we identify two distinct subgroups of OSC based on DRRGs expression. The OSC patients of cluster 1 group has high level of CD4 T cell infiltration, and low level of CD8 T cell and Myeloid dendritic cell infiltration. The stemness features of cluster 1 was also higher than cluster 2. Moreover, we found that the chemosensitivity of cisplatin and docetaxel was higher in cluster 1 OSC patients than cluster 2, which could provide clinicians with a guideline for precise medication of appropriate therapeutic drugs to OSC patients. These results both suggested the significance of the DRRGs in the prognosis, diagnosis, and treatment of OSC patients. However, further experimental validation is needed.

Last, we found MCM3 might be a key protein to promote proliferation and DNA replication in OSC women with alcohol addiction (Fig. 11). Many previous studies indicated that MCM3 played a key role in the development and progression of multiple cancer types, including cervical cancer(47), medulloblastoma(60), salivary gland tumors(61), and prostate cancer(62). Moreover, we also found MCM3 was involved in the development of liver cancer(17) and OSC(18). In this study, we further confirmed that the important molecular regulation mechanism of MCM3 for driving DNA replication aberrant in OSC patients with alcohol addiction.

In this study, we made a systemic analysis for DRRGs and AARGs in the progression of OSC, such as mRNA expression, DNA alteration, survival rate, protein structure, correlation, function enrichment, PPI network, prognostic stratification model, distinct subgroups, stemness features, immune infiltration analysis, EVs regulation, MTT and EdU analysis. In summary, our results indicated that these DRRGs, especially in MCM3, were significantly diagnostic and prognostic biomarkers, and potential therapeutic target for OSC patients with alcohol addiction.

Competing Interests: The authors declare no competing financial interests.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and material

The data used to support the findings of this study are available from the corresponding author upon request

Funding

The present study was supported by the Natural Science Foundation of HuNan Province (2021JJ50070).

Authors’ contributions

H Li, and T Zeng designed the project. D Liu and Ll Xu used online tools. H Li and Ll Xu analyzed the data. T Zeng and J Zou wrote the paper. J Zhang, AB Gao and YK Li revised the manuscript, designed the experiment. All authors contributed to the article and approved the submitted version.

Acknowledgements

Not applicable.

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Identification of key genes associated with alcohol addiction and DNA replication aberrant in Ovarian serous cystadenocarcinoma using an integrated bioinformatics analysis

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Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Methods

Data download

DNA alteration analysis

Functional enrichment analysis

Tertiary structure of protein and PPI network construction

Survival analysis

Prognosis Model Construction

Classification of OSC subtypes

Immunoinfiltration analysis

Stemness index analysis

IC50 score analysis

Cell culture and transfection

Proliferation analysis

Western blot

Results

Confirming the expression of DRRGs in OSC

DNA alteration of DRRGs in OSC

Function of these DRRGs in OSC

The effect of DRRGs on signaling pathways

Analysis of DRRGs expression identifies two distinct subgroups of OSC

The prognostic values for these DRRGs in OSC

Identification of clinical outcomes in OSC based on these DRRGs

Validation of hub DRRGs and AARGs in OSC

MCM3 might facilitate OC cell DNA replication and proliferation by EVs

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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